2009 CSHP National Awards Program Winners Programme national des prix 2009 de la SCPH : lauréats et lauréates
Bibliographic record
Abstract
Purpose: Impediments to optimal delivery of pharmaceutical care include pharmacist shortages, increasing patient acuity and the associated workload.Pharmacy technicians are well positioned to augment clinical service delivery because of their systems and medication use process knowledge.Technicians can support decentralized clinical pharmacists by performing information gathering and other technical tasks required for making drug therapy decisions.We hypothesized that incorporating a Clinical Pharmacy Support Technician (CPST) in our 26-bed adult tertiary level Intensive Care Unit (ICU) team would improve pharmacists' work efficiency.Methods: Patients in the ICU are supported by 2 multidisciplinary health care teams.Each team includes a clinical pharmacy specialist.Pharmacy technicians hired into the clinical support role were experienced with the hospital medication distribution system.CPST training is supervised by the ICU pharmacist and clinical practice leader.Training activities included: job shadowing, supervised guided activities, and group discussions.Protocols and procedures were developed based on the expected competencies.Monthly meetings are arranged for continuing education, skills upgrading, and quality assurance.CPST training includes activities which would best support the ICU pharmacists.These activities include the following: patient specific data collection and monitoring form documentation, screening and tracking patients progress according to targeted parameters, assisted therapeutic drug monitoring services, ward-based troubleshooting, IV compatibility assessment, adverse drug reaction reporting, student orientation, sick call coverage, and traditional medication distribution support in the pharmacy dispensary Results: The CPST program improved work efficiencies in the delivery of pharmaceutical care by reducing the amount of time required for a pharmacist to assess a patient, thereby increasing the number of patients assessed per day and increasing time for pharmacists to perform cognitive-based activities.Conclusion: Clinical Pharmacy Support Technicians, when deployed optimally, are able to increase the work efficiency of pharmacists and ultimately have a role in improving patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.194 | 0.045 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".